Introduction
Dental shade matching is a critical aspect of restorative dentistry, ensuring that artificial restorations blend seamlessly with natural teeth. Accurate shade matching enhances aesthetics and patient satisfaction. In this project, we leveraged artificial intelligence (AI) and RGB values from dental images to classify 16 different dental shades, achieving significant results in terms of accuracy and reliability.
Objective
Our primary goal was to develop an AI-powered machine learning model capable of accurately predicting dental shades based on images of teeth. This model restorations, assists dentists in selecting the correct shade for dental reducing the subjectivity and variability inherent in manual shade matching.
Data Preparation and Feature Extraction
The dataset comprised dental images labeled with one of the 16 predefined dental shades. The images were pre-processed to ensure consistency and quality, with a focus on standardizing size, resolution, and lighting conditions. A crucial step in our pre-processing was cropping the images to focus on the tooth area, minimizing background noise and enhancing feature extraction accuracy.
Key Insights
AI and RGB Features: Utilizing AI to analyze RGB histograms and color moments allowed us to capture both the overall color distribution and specific statistical properties of the images, leading to improved classification accuracy.
Model Performance: Our AI-powered model demonstrated high accuracy in predicting the correct dental shade. The top three predicted classes for each image were presented, providing additional context for decision-making.
Application:
The model's predictions can be integrated into clinical workflows, assisting dentists in making more informed shade selections, ultimately enhancing patient outcomes
Conclusion
This project showcases the potential of AI in dental shade classification, highlighting how RGB values can be leveraged to achieve accurate and reliable results. The integration of such AI models into clinical practice can significantly improve the shade matching process, making it more objective and efficient.
References
Comparative Analysis of Color Matching System for Teeth Recognition, mder-224280 By focusing on the essential aspects of feature extraction, model training, and evaluation, we have demonstrated a practical approach to solving the dental shade classification problem using AI and RGB values. The referenced article provides further context and background to the methods and techniques used in this project.
